DeepSign is a self-learning framework for real-time gestural analysis that provides high speed recognition with great accuracy using minimal hardware and minimal computing power. All that's required is a simple web or mobile camera found in everyday laptop or smartphone.

PROBLEM

There is a significant gap between the needs of people with hearing impairment and the opportunities offered by modern technological solutions. The needs of the deaf are often left behind. It is necessary to make a technological breakthrough in this segment and to translate the barrier-free communication environment to a qualitatively new level.

People with hearing impairment find it difficult to obtain full access to social services and participate in public events.

Attempts to create an effective barrier-free communication environment that uses the capabilities of information technology have so far not been successful.

MARKET

360

32

133

MILLION

MILLION

FEDERATIONS

PEOPLE WITH HEARING IMPAIRMENT IN THE WORLD

OF THEM ARE CHILDREN

NATIONAL DEAF FEDERATIONS

DEEPSIGN

DEEPSIGN is a technological core for our own products, aimed to bring breakthrough technology into various spheres, like new-generation user experience, transportation and logistic, emergency services, military-industrial complex, medicine.

Video

SignLangProcessing

Text

Voice

SignLang App cross-platform

DeepSignbackend

Videofrom cam

HOW DOES IT WORK

ADVANTAGES

Provides high speed real-time recognition with great accuracy using minimal hardware and minimal computing power.

All that's required is a simple web or mobile camera found in everyday laptop or smartphone.

Machine Learning algorithm allows to create intuitive user-friendly interface which could be used with the majority of existing products.

SIGNLANG

ARRM SignLang is a sign language retranslator using the schemes 'gesture-text-voice' and 'voice-text-gesture' based on DeepSign technology. The weight of the product is due to total lack of services for automated direct communication between and feedback from deaf mute users.